SharP is a text‑generation framework that uses both hard and soft prompts to guide large language models in creating task‑specific synthetic news samples for low‑resource fake news detection. The method combines structured factual constraints with learned semantic patterns, and employs a boundary‑aware strategy that focuses on classifier‑uncertain areas to refine decision boundaries. Dual objective optimization balances semantic alignment with source data and classifier feedback, aiming to improve robustness and generalization when labeled data are scarce.
By Ziyu Luo, Wei Li, Haoping Huang, Kecheng Liu, Min Gao
The paper introduces a hybrid BERT‑CNN‑BiLSTM model for simultaneously classifying Bangla news headlines and performing sentiment analysis. Using the BAN‑ABSA dataset of 9,014 headlines, the authors compare two sampling strategies and report headline accuracy up to 81.37 % and sentiment accuracy up to 73.43 %. The proposed framework outperforms baseline models and establishes new state‑of‑the‑art results for Bangla headline classification and sentiment analysis.
By Mirza Raquib, Munazer Montasir Akash, Tawhid Ahmed, Saydul Akbar Murad, Farida Siddiqi Prity, Mohammad Amzad Hossain, Asif Pervez Polok, Nick Rahimi
arXiv:2609.36850v1 Announce Type: new
Abstract: Generative content is increasingly entering the production and dissemination of news, transforming fake news from manually fabricated or simply manipul...
By Wenbin Shen, Guoxuan Qin, Guangxu Yao, Baodong Wang, Yuanbo Rui, Zhichao Lian
arXiv:2507.12295v2 Announce Type: replace-cross
Abstract: Text anomaly detection is a critical task in natural language processing (NLP), with applications spanning fraud detection, misinformation id...
By Feng Xiao, Jicong Fan
arXiv:2608. 05104v1 Announce Type: new Abstract: Deep neural networks have shown impressive success in NLP tasks owing to their complex structure and huge number of edges.
By Sajib Hossain, Md Kamrus Samad, Anan Ghosh, Labib Imam Chowdhury, Nabeel Mohammed
arXiv:2608.22922v1 Announce Type: new
Abstract: We present HelaBERT, a family of two BERT-based masked language models pre-trained from scratch on approximately 1 billion tokens of Sinhala text sourc...
By Thisen Ekanayake, Nisansa de Silva
arXiv:2608. 06732v1 Announce Type: new Abstract: Recent text-to-video (T2V) generation models enable fake news videos to be synthesized from scratch, shifting the threat beyond cheap fakes assembled from existing footage.
By Yifeng Luo, Yupeng Li, Liang Lan, Tian Wang
arXiv:2608.22832v1 Announce Type: new
Abstract: The social interactions among crowds via \textit{Danmaku} (a.k.a., bullet comments) on modern multimedia platforms can facilitate both viewpoint confli...
By Xiansheng Luo, Chaowei Zhang, Zewei Zhang, Yi Zhu, Jipeng Qiang
arXiv:2606. 04199v1 Announce Type: cross Abstract: The increasing use of large language models has raised concerns about the spread of AI-generated fake news, particularly under varying prompting strategies.
By Aya Vera-Jimenez, Samuel Jaeger, Calvin Ibenye, Dhrubajyoti Ghosh
arXiv:2601.03981v3 Announce Type: replace
Abstract: To efficiently combat the spread of LLM-generated misinformation in the news domain, we present RADAR, a Retrieval-Augmented Detector with Adversar...
By Song-Duo Ma, Yi-Hung Liu, Hsin-Yu Lin, Pin-Yu Chen, Hong-Yan Huang, Shau-Yung Hsu, Yun-Nung Chen
MIL-BERT is a neural network algorithm that classifies large texts by selecting relevant excerpts, inspired by multiple instance learning. It scales to samples with nearly 1 million tokens and has been evaluated on seven datasets, achieving state‑of‑the‑art results on three long‑text tasks such as political bias detection, trigger warning identification, and author demographic inference. The model also generalizes from weakly‑labeled text bags to accurately classify smaller instances.
By John Cadigan, Dayne Freitag, Eric Yeh
The paper investigates whether multimodal large language models (MLLMs) can generate and detect realistic multimodal fake news on social media. Using a multi‑agent framework—comprising a story agent, an image agent, and a critic agent—the authors produced over 9,000 paired multimodal news posts across science, health, and entertainment domains. They benchmarked 16 open‑ and closed‑source MLLMs for automated detection and found that most models fall far short of human accuracy, especially in identifying image authenticity, highlighting the need for stronger defenses against social media fake news.
By Jiyao Yang, Yang Liu, Zhenyue Qin, Qingyu Chen, Xiuzhen Zhang